How I Combined RAG, LLaMA, and ElevenLabs to Build an AI Agent That Talks, Thinks, and Acts

From Architect's Desk

Behind the Scenes of Building a Voice-Enabled, Tool-Calling AI Agent Using Open-Source LLMs

In this post, I want to walk you through the exact architecture, tech stack, challenges, and mindset behind building my own custom AI agent one that talks, thinks, and acts.

Ever wondered what it takes to build your own AI assistant that doesn’t just chat but understands, recalls knowledge, speaks like a human, and can call tools to get things done? ChatGPT can hold a conversation, but what if you could build something smarter, grounded in your own data, capable of querying systems, and responding back in a natural-sounding voice?

That's the journey I'm about to break down for you.

Why I Built This

Over the past year, the world of open-source Large Language Models (LLMs) has exploded with incredible models like LLaMA 2 & 3, Mistral, and many others. Despite this, most AI assistants still fall into two camps: either they're chat-only interfaces limited to their pre-trained knowledge, or they rely entirely on proprietary, closed-off cloud APIs.

I wanted something different. I envisioned an AI that was:

• Knowledgeable: It should be able to access my private documents, notes, and internal knowledge bases to provide answers grounded in my data (RAG + Tool Calling).

• Articulate: It shouldn't just respond with text. It should speak back naturally, making the interaction feel seamless and human (Voice via ElevenLabs).

• Sovereign: It should run on my own hardware or be deployable to a cloud instance I control, giving me full ownership and privacy.

This project was born out of a desire to weave these threads together—to create an agent that was truly mine.

System Architecture (At a Glance)

Before we dive deep, here’s a high-level look at how all the pieces fit together. The diagram below shows the journey of a user's query from voice input to spoken response.

Code snippet

flowchart LR
  A[User Voice/Text Input] --> B[ASR: Whisper]
  B --> C[Text Preprocessing]
  C --> D[Embedding & Retrieval (FAISS)]
  D --> E[Context Injection into Prompt]
  E --> F[LLaMA 3 (Fine-tuned)]
  F --> G{Tool Needed?}
  G -->|Yes| H[Tool Router]
  H --> I[API Call / External Tool]
  I --> J[Tool Response]
  J --> K[Final Model Output]
  G -->|No| K
  K --> L[TTS: ElevenLabs]
  L --> M[Audio Response to User]

In simple terms: My voice is transcribed, the text is used to find relevant information from my documents, that context is fed to the LLaMA model, the model decides if it needs to use a tool (like checking my calendar), generates a final answer, and then ElevenLabs converts that text answer into spoken audio.

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Core Components

The magic of this system lies in how five core components work in concert.

1. Whisper (Voice Input)

For the "ears" of the agent, I used OpenAI's Whisper. It provides incredibly high-quality speech-to-text (ASR) conversion. It's robust against background noise, easy to run locally for privacy, and forms the first critical link in the chain.

2. Retrieval-Augmented Generation (RAG)

An LLM only knows what it was trained on. To make it knowledgeable about my data, I implemented a RAG pipeline.

• Embeddings: I used SentenceTransformers to convert my documents and notes into numerical representations (embeddings).

• Vector Search: These embeddings are stored in a FAISS (Facebook AI Similarity Search) index. When I ask a question, FAISS allows for a super-fast search to find the most relevant document chunks.

• Context Injection: The top 3 most relevant chunks of text are then automatically "injected" into the prompt I send to LLaMA, giving it the specific context it needs to answer accurately.

3. LLaMA 3 (The Brain)

The core of the system is a fine-tuned LLaMA 3 (8B) model. This model is responsible for reasoning, understanding the user's intent, and generating a coherent response.

• Inference: I run the model using the highly-optimized llama.cpp engine, which makes it possible to get great performance even on consumer or prosumer-grade hardware.

• Fine-tuning: To make the model better at my specific tasks, I fine-tuned it using LoRA (Low-Rank Adaptation) on a dataset of domain-specific question-and-answer pairs. This sharpens its ability to respond accurately without the massive cost of a full retrain.

4. Tool Calling

This is what gives the agent "hands." Using a schema inspired by LangChain's function calling, I gave the model the ability to use external tools. If my prompt is "What's on my calendar today?", the model doesn't hallucinate an answer. Instead, it recognizes the need for a tool and formats a request to my get_calendar_events function. It can also hit internal company APIs, pull real-time weather data, or perform any other action I define.

5. ElevenLabs (The Voice)

To complete the conversational loop, I used the ElevenLabs API for text-to-speech (TTS). The quality of their voice generation is astonishingly natural. A key feature is their streaming mode, which starts playback almost instantly, cutting latency from several seconds to under one. This is crucial for making the interaction feel responsive and real-time.

Training & Deployment

Training Strategy

I didn't train LLaMA from scratch. The strategy was to adapt the powerful base model to my needs.

• I created a dataset of Q&A pairs in JSON format, reflecting the kinds of questions and tool requests I would be making.

• Using the Hugging Face PEFT (Parameter-Efficient Fine-Tuning) library, I trained LoRA adapters. This approach modifies only a tiny fraction of the model's weights, making fine-tuning fast and computationally cheap.

• The primary goal during validation was to improve answer consistency and dramatically reduce any instance of the model hallucinating an answer when it should have used the RAG context.

Deployment Stack

• Inference: llama.cpp running on a RunPod cloud instance with an NVIDIA A100 GPU.

• Backend: A FastAPI server orchestrates everything—it receives the initial request, calls the RAG pipeline, queries the LLaMA model, handles tool routing, and sends the final text to ElevenLabs.

• Tooling: A combination of LangChain for the function-calling schema and a custom ToolRouter class to manage the available tools.

• Storage: FAISS for vector storage, SQLite for metadata, and an S3-compatible object store for documents and logs.

• Voice: ElevenLabs Streaming API for low-latency TTS.

Key Challenges & Solutions

Building a system this complex wasn't without its hurdles. Here were the biggest ones:

• ❌ Challenge: Tool Calling Hallucinations. The model would sometimes "hallucinate" a tool call, making up a fake API response instead of executing the actual tool.

  • ✅ Solution: I engineered a more explicit system prompt that clearly defined the available tools, their schemas, and strict instructions on when and how to use them. This scaffolding significantly improved reliability.

• ❌ Challenge: Voice Sync Delay. In the initial version, the user had to wait for the entire audio file to be generated by ElevenLabs before hearing a response, which felt slow and clunky.

  • ✅ Solution: Switching to the ElevenLabs streaming API was a game-changer. The audio starts playing almost immediately, creating a much more natural conversational flow.

• ❌ Challenge: Latency During Context Retrieval. Searching through thousands of document chunks for every query was adding a noticeable delay.

  • ✅ Solution: I implemented a caching layer for embeddings and used a top-k prefetching strategy to anticipate common queries, which cut down the retrieval time significantly.

Final Thoughts

Building this system gave me a deep appreciation for a crucial concept in modern AI engineering: orchestration. The magic isn't just in the power of the LLM itself. It's in the tight, carefully choreographed dance between all the componentsthe RAG system providing knowledge, the fine-tuning providing domain-specific skill, the tools providing action, and the voice APIs providing a natural interface.

This AI isn’t just chatty it knows, it acts, and it talks. It's a proof of concept for a future where we can all build personalized, powerful AI agents that work for us, with our data, and in our own voice.

Try It Yourself

Want to build something similar? The ecosystem of open-source tools has never been better.

• Start by exploring llama.cpp, LangChain, Whisper, and the ElevenLabs API.

• Dropping the GitHub boilerplate Early Next Week for this project. Please Do Subscribe :)